Risk-Sensitive Filtering under False Data Injection Attacks

This paper addresses a risk-sensitive remote estimation problem for cyber-physical systems (CPSs) where the accurate model of a dynamic system is not completely known or may differ from the assumed model. In CPSs, sensors and the monitoring control center are remotely located. Sensors transmit the measurements via unreliable wireless communication channels that are vulnerable to cyber-attacks. Specifically, attackers can inject false data to alter the measurements in the communication channel or attack sensors. To tackle this, we design a risk-sensitive filtering algorithm to operate under false data injection attacks. The proposed estimator aims to minimize the risk-sensitive error criterion, defined as the expectation of the accumulated exponential quadratic error. Simulation results demonstrate the effectiveness of the proposed algorithm.